Global maps need to be updated in almost real-time and be reliable, especially when such maps directly influence vehicle controls (e.g., emergency braking). In certain places, such as outside urban centers, the accuracy of maps may tend to be lower and have rarer updates performed by map providers. Municipalities are typically in charge of dynamic map updates (e.g., road works), and the channels to offer the latest vehicle navigation systems are not always clear. Often, the locations of road edges change over time, especially considering gravel roads and snow piles on the edges of roads, or the like. However, current and accurate information regarding the position of road edges is important for use by autonomous vehicle computing systems, for example to allow accurate calculation of when a pedestrian or bicycle (or the like) is on a collision path that requires a reaction by the autonomous vehicle.
Identification of dynamically appearing and disappearing obstacles in front of the vehicle is one of the major problems for increasing the operating speeds of automated vehicles. The automotive industry is working with autonomous features by delegating part of the decision making to computers, and therefore dynamic map layers have become more important. One suggested solution is to use cloud based IT systems to distribute the data, but these still represent response times on the order of 5-10 minutes in map updates (see Elektrobit. 2015. Map updates for autonomous driving—the sensor cloud approach. Slides of the Automotive World Webinar. 15 Dec. 2015). The challenges may be twofold: 1) How to improve safety by avoiding accidents, and 2) How to minimize the number of false alarms which may result in disturbing the traffic flow.
Examples of these dynamic obstacles are short duration incidences which may for example be animals appearing in front of the vehicle or a pedestrian accidentally falling while crossing the street, which may even result in the pedestrian disappearing from the vehicle's electronic horizon in front of the vehicle. Such events may happen in less than 1 second, and relevant information should be added to the dynamic map layers of all vehicles in the area, but without causing false alarms if the obstacle(s) truly disappears quickly. Otherwise the automatic traffic may become too inefficient, annoying for passengers, and vulnerable to intentional disturbances.
Existing work includes U.S. Pat. No. 8,594,923 B2; U.S. Pat. No. 6,230,098 B1; U.S. Pat. No. 5,614,895 A; EP Patent number 20140157579; US Patent App. No. 20150193988 A1; U.S. Pat. No. 8,467,928 B2; US Patent App. No. US20110169625 A1.
Other resources related to collaborative perception include the Connected Vehicle Reference Implementation Architecture project and the Collaborative Vehicular Perception project at Trinity College Dublin.